FLC-Based Adaptive Neuro-Fuzzy Inference System for Enhancing the Traveling Comfort

In this paper, pioneering adaptive neuro-fuzzy inference system (ANFIS) which is trained with the data obtained from well-known intelligent control technique fuzzy logic controller (FLC) for half-car (HC) model is proposed to improve the traveling comfort. In automobile industries, the traveling performance of the vehicle is tested at the design stage by simulating the vehicle response to various road excitations under different loading conditions. In this work, the disturbance from the road is assumed to be a dual bump. Initially, a FLC is designed to give better performance. Secondly, a flexible machine learning approach artificial neural network (ANN) that is trained with the FLC data by considering the performance measure as mean square error (MSE) is designed and used. At last, an ANFIS with the adaptive and generalizing features of ANN and intelligence of FLC is used for control purpose. In the modeling of system, simulation with and without controllers is carried out in MATLAB/Simulink environment. A comparison is made among the responses of the system with these controllers, and it shows that the system with FLC-based ANFIS gives significant reduction of the body acceleration (BA) and thus improves the traveling comfort.